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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 57:M741-M746 (2002)
© 2002 The Gerontological Society of America

Undernutrition and Risk of Mortality in Elderly Patients Within 1 Year of Hospital Discharge

Longjian Liua, Melinda M. Boppa, Paula K. Robersonb and Dennis H. Sullivana

a Geriatrics Research, Education, and Clinical Center, Central Arkansas Veterans Healthcare System, and Donald W. Reynolds Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock
b Division of Biometry, University of Arkansas for Medical Sciences, Little Rock

Dennis H. Sullivan, Geriatric Research Education and Clinical Center (182/LR), Central Arkansas Veterans Healthcare System, 4300 West 7th Street, Little Rock, AR 72205 E-mail: SullivanDennisH{at}uams.edu.


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
Background. The importance of undernutrition as a determinant of postdischarge mortality among hospitalized elderly people remains controversial. The purpose of this study was to investigate this issue.

Methods. The study included 660 elderly patients (85% white, 98% men, average age 73 ± 6 years) discharged from a university-affiliated Department of Veterans Affairs Hospital, who were followed for 1 year. Associations between patient characteristics at hospital discharge and mortality were identified utilizing Cox Proportional Hazards Regression analysis.

Results. In the year following hospital discharge, 85 subjects (13%) died. After adjusting for illness severity (Acute Physiology and Chronic Health Evaluation II score) and functional status (Katz Index of Activities of Daily Living score), a body mass index (BMI) <=20 kg/m2 was strongly associated with mortality (adjusted relative risk, [95% confidence interval] 1.83 [1.17–2.85]), as was more than 10% weight loss in the prior year (2.31 [1.35–3.94]), and weight as percent of usual weight (WPU) <=85% (1.78 [1.14–2.77]). Albumin <=30 g/l was only weakly associated with mortality (1.10 [0.67–1.81]). When all of the putative nutrition variables were included in a multivariable analysis with the two control variables, only BMI followed by WPU <=85% entered the model. Utilizing this model, the predicted probabilities of death at 1 year were calculated for the study subjects and for a hypothetical group of patients who were identical to the study subjects except they were assigned a BMI of 28 kg/m2 and their WPU was 100%. Compared to 24% of the actual subjects, only 7% of the hypothetical well-nourished patients would have been classified as being at high risk for mortality (a 71% relative reduction).

Conclusions. Older patients who have evidence of chronic body mass depletion are at significantly increased risk of mortality within the year following hospital discharge.

PROTEIN-energy undernutrition may be an important determinant of long-term outcomes among older patients discharged from acute care hospitals. However, there is only indirect evidence to support this conclusion. Studies of older hospitalized patients indicate that many of these patients meet commonly used assessment criteria for being undernourished at the time of their hospital discharge (1)(2)(3). These assessments include the use of serum secretory proteins, anthropometric measurements, and weight indices. Among older patients discharged from rehabilitation units, abnormalities of these putative nutritional indicators are predictive of an increased risk of mortality within the subsequent 1 to 3 years (4)(5)(6)(7). Controlling for diagnoses, functional abilities, and other indicators of illness severity does not change these relationships. Studies of older patients being discharged from acute care hospitals have also found strong associations between global measures of nutritional status and subsequent mortality (8)(9). However, the relative importance of specific nutritional indicators remains uncertain. It is also unclear whether the association between nutritional status and subsequent mortality is confounded by life-threatening conditions such as advanced cancer. Further studies are needed to clarify these issues.

To investigate the interrelationship between nutritional status, illness severity, and clinical outcomes, we conducted a prospective study of nonterminally ill elderly patients who were being discharged from an acute care hospital. The specific objective was to determine whether elderly patients who are protein-energy undernourished at discharge are at increased risk for death during the year following hospitalization and whether the association between protein-energy undernutrition and risk remains significant after controlling for functional status and other indicators of illness severity.


   Methods
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 Abstract
 Methods
Results
 Discussion
 References
 
Participants
From January 1994 to February 1997, all patients 65 years or older admitted to a general medical or surgical ward of a university-affiliated Department of Veterans Administration (VA) Hospital were screened within 12 hours of admission to determine eligibility. Patients with metastatic cancer and those receiving palliative care for other terminal conditions were excluded because such patients may be expected to receive less aggressive nutritional interventions. Remaining patients were assigned a random number. To maintain an enrollment rate of three to four patients per week, only those with a number below a preestablished cutoff were asked to enter the study. Of the 678 participants, 18 died in the hospital. The remaining 660 patients represented the study population and were tracked for 1 year after discharge.

Patient Evaluations
Within 48 hours of admission, each subject completed a standardized diagnostic evaluation, including basic medical, neuropsychological, functional, and nutritional assessments. At hospital discharge, repeat medical, functional, and nutritional assessments were completed. The details of this evaluation have been published elsewhere (1). In the present study, three groups of nutritional indicators, measured at discharge, were considered: (i) weight indices, including body mass index (BMI) and history of weight loss; (ii) anthropometrics measurements, including mid-arm circumference and mid-arm muscle circumference; and (iii) serum markers, including prealbumin, albumin, total protein, and total cholesterol. Subsequent to discharge, all subjects were tracked via telephone for 1 year. When necessary, the VA computer system was used to find updated phone numbers for subjects who changed residences during the year of observation.

To determine whether both the amount and duration of weight loss are related to 1-year mortality risk, three indicators of weight loss were evaluated: (i) Weight as a percent of usual weight (WPU) and the amount of weight lost during the year prior to discharge were included as indicators of long-term weight loss; (ii) The amount of weight lost within 6 months prior to discharge was used as an indicator of short-term weight loss; and (iii) Weight before onset of current medical problems and at least 2 years prior to admission were used as definitions of usual weight. Only weights that could be documented through review of old medical records were used in the study.

As indicated by prior studies, demographic characteristics, substance (alcohol and tobacco) abuse history, functional and cognitive status, and illness severity indices are important correlates of mortality (3)(8)(9)(10)(11)(12)(13)(14). Because all of these factors may confound the relationship between nutritional status and mortality, we evaluated them as potential control variables in the multivariate analyses. A patient was considered a current smoker if he or she smoked one or more packs of cigarettes per week within the 3 months prior to admission. Confirmation of smoking status was based on patient query and medical record review. A diagnosis of alcohol abuse was taken from the medical record and was defined as health or social problems resulting from excess alcohol consumption within the prior 5 years. Functional and cognitive status were assessed using the Katz Index of Activities of Daily Living (ADLs) scale (15) and the Mini-Mental State Examination score (16), respectively. Illness severity was assessed using three validated indices: the number of chronic conditions, the Charlson Weighted Index of Co-morbidity (17), and the Acute Physiology and Chronic Health Evaluation (APACHE) II (18). Demographic variables assessed included education, race, and marital status.

Statistical Analyses
The relationship between the patient characteristics and mortality were examined using univariable analyses (i.e., Student's t test for comparing means, the {chi}2 test for dichotomized parameters) (19)(20). Variables that showed skewed distributions were log-transformed prior to entry into the analyses. BMI, WPU, and the other putative nutrition indicators were also categorized as dichotomous variables. The cutoff points for BMI, WPU, cholesterol, albumin, and total protein were selected based on previous studies and commonly used reference ranges (10)(21)(22). Because adequate population norms were not available for mid-arm circumference and mid-arm muscle circumference, the cutoff point was based on the 25th percentile of the study population.

In the second group of analyses, the relationship between each putative nutrition variable and mortality was analyzed using Cox proportional hazard regression for assessing relationships with time to death. If a given nutrition variable was found to be significantly associated with the outcome by univariable analysis, a series of additional multivariable analyses were performed. The additional analyses were conducted to determine if the putative nutrition variable remained significantly associated with the outcome after controlling for (i.e., forcing into the model) demographic factors, functional status, and illness severity at discharge.

In the third group of analyses, the strongest combination of nutritional predictors was identified. This was done by forcing the control variables into the Cox regression model, then using the stepwise procedure to select the strongest combination of nutritional predictors from all of the putative nutrition variables we examined. Survival curves were then generated based on the estimates of the survivorship function as generated by the Cox regression model for specific sets of covariate values. The original data were also run back through the retained Cox regression model. The output included the predicted probability of survival at 1 year for each study subject. Based on these probabilities, the subjects were classified into one of three 1-year mortality risk strata: high, moderate, or low. To estimate the theoretical contribution of undernutrition to risk, the original data were again run back through the retained Cox regression model. However, on the second run, each subject's nutrition variables were set to a normal value (see Results). The output included each subject's predicted probability of death at 1 year given that the subject's illness severity remained the same but nutritional status was normal. All data analyses were conducted using SAS software (version 8.0, SAS, Cary, NC) (20). A two-sided value of p < .05 was considered significant.


   Results
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 Abstract
 Methods
 Results
Discussion
 References
 
Basic Characteristics and Univariable Analysis
The study population had an average age of 73 ± 6 years (range 65 to 99 years). The majority were white (85%) men (98%). None of the 660 study subjects were lost to follow-up in the year of observation. The 10 most prevalent active medical problems at admission are shown in Table 1 . More than 40% of the subjects had a problem in one or more of the following disease categories: hypertension, coronary heart disease, pulmonary disease, arthritis, or gastrointestinal disease.


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  		Table 1. Most Prevalent Active Medical Problems

 
Within the year after hospital discharge, 85 subjects (13%) died. As shown in Table 2 , many of the discharge patient assessment variables obtained in this study were significantly associated with mortality risk (p < .01 or p < .001). The notable exceptions included serum prealbumin, total protein, and total cholesterol concentration, current smoking status, and alcohol abuse (p > .05).


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  		Table 2. Characteristics of Study Subjects by Outcome Group

 
Cox Proportional Hazards Regression Analysis
As shown in Table 3 (Models 1–3), illness severity (APACHE II score) and functional status (Katz Index of ADL score) confound the associations between each of the putative nutritional indicators and mortality. However, the extent of the confounding varied. The relationships between mortality and the indicators of body mass and long-term weight loss (e.g., BMI, weight loss > 10% in the prior year, weight <= 85% of usual weight) remained clinically and statistically significant after adjusting for the two control variables (Model 3). The importance of albumin and short-term weight loss as predictors of mortality is less certain due to the wide confidence intervals for the adjusted relative risk for each of these variables. The addition of demographic variables (age, race, education, marital status) to the multivariable analyses did not appreciably change the relationships between the nutritional variables and mortality (data not shown). Forcing this number of variables into the analyses also raised the concern that this would produce over-fitted models (23). For these reasons, the demographic variables were not included in any of the multivariable analyses.


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  		Table 3. Putative Nutrition Variables and the Associated Risk of Mortality Within 1 Year of Hospital Discharge

 
After forcing the control variables (APACHE II score and Katz Index of ADL score) into the Cox regression model, a stepwise procedure was utilized to select the strongest combination of nutritional predictors. Of the 21 putative nutrition variables from Table 3 that were included in the analysis, only BMI (<=20 kg/m2) followed by weight as a percentage of usual weight (WPU <=85%) entered the model. This Cox regression model was retained. Using the retained model, the estimated survivorship function for a hypothetical patient was contrasted with that of another patient who differed only in having a lower discharge BMI. These results are depicted graphically in Fig. 1. By controlling for the other three variables in the model, the independent effect of BMI on mortality is demonstrated. There is a similar relationship between weight as a percent of usual weight and survival (data not shown).



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  		Figure 1. Estimates of the survivorship function as generated by the Cox regression model for specific sets of covariate values. The upper curve is for body mass index (BMI) > 20 kg/m2 with all of the other independent variables in the model set equal to their population means. The lower curve was produced in the same manner except it is for BMI <=20 kg/m2 (see text for details).

 
Estimating the Theoretical Contribution of Undernutrition to Mortality in the Study Population
By running the original data through the retained Cox regression model (as per above), the estimated probability of survival at 1-year posthospital discharge was calculated for each study subject. Based on these estimates, subjects were categorized into one of three risk strata: high, moderate, or low. The ranges of estimated survival probability utilized to define each strata were <0.85, 0.85–0.93, and >=0.94, respectively. As indicated in Table 4 , 24% of discharged patients fell into the high-risk strata. To estimate the theoretical contribution of undernutrition to mortality risk, these same analytic steps were repeated. However, in the second group of analyses, each subject was assigned a BMI = 28 kg/m2 and a stable body weight (WPU = 100%). These values were chosen based on the findings from prior longevity studies that indicate that a BMI = 28 kg/m2 and a stable body weight (WPU = 100%) would be desirable for elderly adults (2)(6). Given these assumptions, the percentage of subjects within the highest risk strata decreased from 24% to 7% (a 71% relative reduction). The percentage of subjects within the moderate-risk strata decreased from 57% to 55% (a 4% relative reduction) (Table 4 ).


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  		Table 4. Theoretical Impact of Undernutrition on Mortality Risk

 

   Discussion
Top
 Abstract
 Methods
 Results
 Discussion
References
 
This study demonstrates that being undernourished at the time of hospital discharge is an independent risk factor for 1-year mortality in elderly patients. The severity of the nutritional depletion was assessed using BMI, weight loss indices, and arm circumferences. Even after controlling for functional status and other indicators of illness severity, the relationship between each of these nutritional indices and the outcome remained significant—the more nutritionally depleted, the greater the risk. Prior studies have shown a subjective assessment of malnutrition at hospital discharge to be strongly associated with increased 1-year mortality after controlling for indicators of illness severity (8). Our study establishes that easily obtained objective measures of nutritional status are strong independent predictors of mortality and can be used to identify high-risk patients.

We found indicators of chronic nutritional depletion (e.g., low BMI and long-term weight loss) to be the best predictors of postdischarge mortality. These findings are consistent with those of other studies (24)(25)(26)(27). Mowe and colleagues (24) found reduced nutritional status to be present in the elderly population prior to the onset of the acute illness that resulted in hospitalization. Based on this finding, these authors hypothesized that many elderly adults become nutritionally depleted as a result of chronic conditions, and this places them at increased risk of subsequent hospitalization. Tellado and colleagues (25) demonstrated that body cell mass was the most powerful predictor of mortality in intensive care unit patients even after adjusting for albumin, age, and diagnosis. Body mass and mortality risk were inversely related: the lower the body cell mass, the higher the mortality. Similar to low BMI and weight loss, low body cell mass is an indicator of chronic nutritional depletion. With nutritional depletion, there may be inadequate reserves of both fat and lean body mass. However, it is probably the loss of lean body mass, particularly muscle mass, that has the most detrimental impact on outcomes of acute hospitalizations. During acute illness, excess levels of protein are burned to meet energy demands of the body (28). Because diet is usually poor, an already compromised lean body mass must be used as a reservoir of energy. This is a possible reason why elderly patients who have experienced depletion of lean body mass have a more difficult time coping with acute illness than better-nourished patients. Their diminished body mass reserves are not able to support their energy demands. Once a critical level of body mass is lost, the chance of survival diminishes dramatically (29). Given the importance of body mass reserves to health and survival during illness, nutritional interventions need to focus on restoring lost reserves. This will probably require long-term aggressive interventions because of the difficulty of rebuilding an older individual's body mass (30)(31).

Unlike the body composition indices, serum albumin, prealbumin, and cholesterol appear to be less reliable independent predictors of mortality among hospitalized elderly subjects. As was found in prior studies, albumin was a powerful predictor of mortality in univariate analysis (32). However, after controlling for illness severity and functional ability in the multivariable analyses, the relationship between serum albumin and mortality became less certain. When albumin is dichotomized at the cutoff value of 30 g/l, the adjusted relative risk is 1.1 with a nonsignificant confidence interval. When 25 g/l is taken as the cutoff value, there is again a wide confidence interval about the adjusted relative risk of 1.38. It is known that inflammation and other disease states influence the concentration of serum albumin (33). This may account for our findings. Serum albumin may reflect acute changes taking place in the patient as a result of his or her illness. Given the wide confidence intervals about the relative risk associated with prealbumin and cholesterol, their importance as risk indicators is also uncertain.

This investigation sheds light on our understanding of the complicated relationship between nutritional status, illness severity, and mortality. Undernutrition may add to the level of risk associated with functional disability and illness. Of the nearly 161 patients identified to be at high risk for mortality, our prediction model suggests that poor nutritional status was an important contributing factor placing 71% of those individuals at that level of risk. It is unknown whether long-term aggressive intervention to improve nutritional status would reduce this population's risk of mortality. Interventions would probably have to start prior to hospitalization. Even though a strong association between undernutrition and mortality exists, an intervention study is needed to address the question of causality and give clinicians a basis for aggressively addressing the problem of undernutrition in this population. Given the gender composition of this study population, further study is also needed to determine whether the results pertain to women.


   Acknowledgments
 
This study is supported by a grant from the Department of Veteran's Affairs, Health Services Research and Department Service.

We acknowledge Jacqueline Rees, MA, from the Donald W. Reynolds Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, for her careful review and editing of this manuscript.

Received January 28, 2002

Accepted June 20, 2002


   References
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 Abstract
 Methods
 Results
 Discussion
 References
 

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